Search Results (17,108)

Background: Obesity represents a major public health challenge worldwide and contributes substantially to the burden of type 2 diabetes and hypertension. While body mass index (BMI) is widely used in clinical practice, indices reflecting central adiposity may provide additional prognostic value. This study aimed to assess the prevalence of general and central obesity in an adult population across different age groups from Stara Zagora, Bulgaria, and to examine their associations with cardiometabolic outcomes and lifestyle factors. Methods: A quasi-representative cross-sectional study was conducted among 3512 adults (mean age 53.7 ± 14.9 years). Anthropometric indices, including BMI, waist circumference, waist-to-hip ratio, and waist-to-height ratio were measured. Cardiometabolic outcomes included diabetes, hypertension, and their combined presence. Multicollinearity was assessed using the Variance Inflation Factor (VIF), and the discriminatory ability of indices was evaluated using Receiver Operating Characteristic (ROC) analysis and DeLong’s test. Results: The prevalence of overweight/obesity (BMI ≥25) was 68.4%, while central obesity (WHtR ≥0.5) affected 66.9% of participants. BMI demonstrated the highest discriminatory ability in this dataset for hypertension (AUC = 0.852) and diabetes (AUC = 0.796), significantly outperforming WC and WHR (p < 0.05). However, 24.4% of individuals with normal BMI exhibited high-risk central adiposity. Significant sex-specific differences were observed: short sleep duration (<6 h) was a strong predictor of obesity in women (aOR = 2.98), whereas smoking showed stronger associations in men. Age-stratified analyses revealed that while BMI stabilizes in the oldest age group (75–89 years), WHtR continues to increase, reflecting age-related redistribution of visceral fat. A strong protective effect of physical activity was observed, supported by quasi-complete separation in active subgroups. Conclusions: General and central obesity represent a substantial health burden in this urban population. While BMI remains a robust screening tool, the integration of WHtR enhances the identification of “hidden” cardiometabolic risk particularly in older adults and individuals with normal BMI. Given the quasi-representative nature of the sample, these findings are primarily generalizable to similar urban populations and may inform targeted regional public health strategies. Full article

This study investigates the relationship between banking sector stability and economic growth in Ethiopia, employing a dynamic panel data approach with the Two-Step System Generalized Method of Moments (GMM). The analysis uses a balanced dataset from 13 Ethiopian commercial banks covering 2014 to 2023, gathered from the World Bank database, the National Bank of Ethiopia, and audited financial statements. Banking sector stability is assessed using indicators such as Z-score, non-performing loan (NPL) ratio, capital adequacy ratio (CAR), liquidity ratio (LR), return on assets (ROA), and loan-to-deposit ratio (LDR), along with key macroeconomic and institutional factors. The results show that banking stability, as indicated by Z-score, liquidity ratios, and profitability, has a positive and significant effect on economic growth, confirming the sector’s role in promoting development. Surprisingly, a positive correlation between NPLs and economic growth suggests unique structural features in the Ethiopian banking system that warrant further investigation. Other variables, such as inflation rates, government expenditure, and gross domestic savings, positively influence economic growth, whereas foreign direct investment is negatively associated with it. The study highlights the importance of enhancing the stability of the banking sector by implementing robust regulatory frameworks, prudent risk management practices, and improved profitability to support sustainable economic development in Ethiopia, while calling for additional research into the unexpected effects of NPLs and FDI amid ongoing financial reforms. Full article

The widespread integration of wind energy conversion systems has fundamentally reshaped modern power grid architecture. However, the limited dynamic response of wind turbine (WT) converters during grid faults—particularly their inability to provide sufficient reactive current and maintain voltage stability under severe dips—necessitates a redefinition of the conventional low-voltage ride-through (LVRT) curve. This study addresses this challenge by proposing a Mutation-Adaptive Mean Variance Mapping Optimization (A-MVMO) algorithm for the control of grid-side converters (GSCs) in wind farms (WFs). To systematically assess post-fault voltage recovery, a Time-Segmented Analysis for Voltage Recovery (T-SAVR) approach is developed with a multi-objective function. The performance of the proposed A-MVMO is benchmarked against standard MVMO and conventional particle swarm optimization (PSO) under both moderate (0.7 pu) and severe (0.15 pu) voltage dips using the IEEE 39-bus system implemented in DIgSILENT/PowerFactory. The results demonstrate that A-MVMO achieves fast, oscillation-free voltage recovery with negligible overshoot (<1%) and lower current injection than PSO and MVMO, while satisfying all engineering constraints. Moreover, the co-optimization of Park-level and turbine-level controllers ensures seamless coordination, as evidenced by the close tracking between the farm-wide reactive power reference and the aggregated turbine response. The T-SAVR method proves essential for focusing optimization on controllable recovery dynamics, yielding a superior LVRT curve. Full article

Fibrin gel, a protein-based polymer naturally generated during coagulation, has garnered attention in the biomedical field for applications such as fibrin glue, due to its specific physical and biological properties. Despite it, low mechanical strength and rapid degradation limited its utilization for biomedical applications. This study presents a reproducible protocol for the synthesis of pure fibrin hydrogels, aimed at achieving predictable structural properties through the precise calibration of fibrinogen and thrombin concentrations. By examining the mechanical and morphological characteristics, as well as the relationship between reagent concentrations and structural integrity, this research assesses impacts on swelling behavior, water absorption, and overall stability. Through a comprehensive analytical approach, we identified an optimal formulation, specifically 2.25 mg/mL fibrinogen and 1.375 U/mL thrombin, that effectively balances structural integrity with high cytocompatibility. The results demonstrate that this calibrated approach ensures high procedural reproducibility and a well-defined hydrogel architecture without the need for exogenous chemical cross-linkers. This work provides a robust methodological framework to overcome the common lack of reproducibility in fibrin-based hydrogel studies, positioning these materials as highly reliable candidates for advanced 3D in vitro models and biomedical applications. Full article

Seafloor hydrothermal systems at mid-ocean ridges are focal points for heat and matter exchange between the seawater and lithosphere. While seafloor seismographs (OBS) and pressure recorders (BPR) are standard for regional monitoring, achieving high-precision, vertical sub-surface data in complex hydrothermal terrains remains a significant technical objective. This study presents a novel in situ penetration probe designed for multi-parameter monitoring of marine hydrothermal vent areas. A key innovation of this work is its operational versatility and engineering efficiency: the probe is specifically designed for post-drilling deployment in boreholes, effectively utilizing existing coring sites to achieve direct coupling with the deep-seated crust, or for targeted placement via Remotely Operated Vehicles (ROVs). The device integrates a titanium-alloy conical tip and cylindrical chamber, housing tri-axial accelerometers and dual temperature-pressure sensors. Numerical simulations using the SST k-ω turbulence model and finite element analysis optimized the cone aperture and assessed fluid–structure stability under deep-sea conditions. Laboratory vibration tests and shallow-water sea trials validated the probe’s basic dynamic response, electromechanical integrity, and capability to acquire coupled environmental parameters. This compact, modular design provides a scalable and cost-effective framework for precise three-dimensional observation of sub-surface hydrothermal processes and deep-sea resource exploration. Full article

In contemporary debates on the United Nations Sustainable Development Goals, there is growing recognition that artificial intelligence (AI) may contribute meaningfully to SDG 2 (Zero Hunger), particularly by enhancing the efficiency of food aid distribution and resource allocation. However, such optimism must be critically situated within the broader institutional and ethical contexts in which AI operates. This study argues that the effectiveness of AI in conflict-affected settings is contingent not only on technical capacity but also on governance structures, ethical safeguards, and institutional trust, dimensions closely aligned with SDG 16 (Peace, Justice, and Strong Institutions). Using the Gaza Strip as a case study, this article demonstrates that AI-driven food assistance mechanisms may inadvertently reinforce structural vulnerabilities. Specifically, algorithmic targeting of aid risks deepening dependency, exacerbating digital exclusion, and weakening already fragile governance systems. The absence of robust data accountability frameworks further complicates these dynamics, raising concerns regarding transparency, fairness, and long-term sustainability. The findings caution against privileging technical efficiency at the expense of socio-political stability. Rather, they highlight that the sustainability of AI interventions in humanitarian contexts fundamentally depends on the credibility and legitimacy of institutions. Accordingly, this study proposes a conceptual model for AI in hunger relief and digital humanitarianism that integrates technical innovation with institutional accountability and social trust. This study presents a narrative review informed by structural searching that examines the influence of AI on food security interventions in fragile contexts. This analysis applies a combined ethical governance and sustainability lens to assess current applications and risks. This research advances a broader analytical framework that moves beyond purely technical interpretations of AI, emphasizing its role as a socio-political tool, through identifying five key pillars for sustainable AI governance: data sovereignty, algorithmic accountability, inclusive system design, community-led governance, and market integrity. Full article

Soil health is critical for the sustainability of tropical plantation ecosystems, However, the ecological factors driving productivity gradients remain inadequately understood. This study investigated rubber plantations on Hainan Island with varying yield levels to assess soil health and its underlying ecological mechanisms using a minimum data set (MDS) approach. Twenty-seven soil physical, chemical, and biological indicators were analyzed at two depths (0–20 cm and 20–40 cm). Principal component analysis identified seven key indicators for the MDS: soil organic matter (OM), alkaline-hydrolyzable nitrogen (AN), cation exchange capacity (CEC), dissolved organic carbon (DOC), microbial biomass phosphorus (MBP), acid phosphatase activity (ACP), and microbial diversity (Shannon-Wiener index, SHDI). The soil health indices derived from the MDS showed strong correlations with those generated from the total data set (TDS) (p < 0.001), confirming the reliability of the MDS framework. Overall, soil health levels were rated low to moderate with no significant differences across low-yield plantations (≤900 kg·ha⁻¹), medium-yield plantations (900–1200 kg·ha⁻¹), and high-yield plantations (≥1200 kg·ha⁻¹)., suggesting a decoupling of soil health and rubber productivity under uniform management practices. Random forest analysis identified microbial-driven phosphorus cycling, particularly MBP and ACP, as the primary determinant of soil health across soil layers, with DOC and SHDI also contributing significantly. These findings highlight the critical role of microbial-mediated nutrient cycling in maintaining soil health in rubber plantations and suggest that current management practices prioritize short-term yields over long-term soil ecological stability. Enhancing microbial activity and increasing organic matter inputs may be essential for improving soil health and ensuring the sustainability of rubber production in tropical agroecosystems. Full article

Financial distress prediction remains a central topic in corporate finance and risk management, with extensive research devoted to improving classification accuracy through increasingly sophisticated statistical and machine learning techniques. Nevertheless, the influence of data preparation on predictive performance has received comparatively less systematic attention. This study examines how an economically grounded data-preparation process affects the predictive performance of selected statistical and machine-learning models dedicated to predicting corporate financial distress. Using the chosen financial ratios, generally accepted indicators of corporate financial stability and economic performance, financial distress models are estimated on both raw, unprocessed input data and pre-processed data involving the exclusion of economically implausible accounting values, treatment of missing observations, and class balancing. In light of the above, the study adopts a structured methodological approach to assess the predictive performance of selected classification models, namely decision tree algorithms (CART, CHAID, and C5.0), artificial neural networks (ANNs), logistic regression (LR), and linear discriminant analysis (DA), using confusion-matrix–based evaluation and a comprehensive set of evaluation measures. The results suggest that the process of input data preparation is a critical factor, significantly improving the predictive performance of financial distress prediction models across most modelling techniques employed. The most pronounced gains are observed in decision tree models. ANNs also demonstrate marked improvement after input data preparation, whereas LR benefits more moderately, and linear DA remains limited despite preprocessing. The average gain in accuracy across all six modelling techniques, calculated as the difference between pre-processed and raw performance for each method and averaged across methods, was approximately 15.6 percentage points, with specificity improving by approximately 26.9 percentage points on average, amounting to roughly half the performance variation attributable to algorithm choice, which underscores that data preparation is a primary determinant of model reliability alongside algorithm selection. A step-level detailed analysis further shows that missing value imputation is the dominant driver of improvement for tree-based models, while class balancing contributes most for ANNs and logistic regression. The findings highlight that reliable financial distress prediction depends not only on technique selection but also on the consistency and economic plausibility of the input data, underscoring the central role of structured data preparation in developing robust early-warning models. Full article

The Technology Acceptance Model (TAM) has been widely used to explain e-banking and digital technology adoption. Existing literature supports the robustness of its core relationships, but the magnitude of the effects varies considerably across studies, raising questions about their stability and generalizability in new contexts. Existing meta-analysis studies focus primarily on pooled effect sizes, providing limited insight into the temporal stability of relationships, their sensitivity to individual studies, and the extent to which observed heterogeneity reflects contextual variation. This study contributes by reinterpreting heterogeneity not as a problem to be reduced, but as a feature that defines the limits of generalizability. We advance the TAM literature by moving beyond average effects and rethinking empirical evidence through the joint lens of robustness, stability, and dispersion. We conduct a random-effects meta-analysis on 44 effect sizes (correlation coefficients) coming from 43 research papers indexed in Web of Science and Scopus. In addition to pooled correlations, the analysis employed cumulative meta-analysis, leave-one-out influence diagnostics, prediction intervals, and publication bias assessments to evaluate the evolution, consistency, and variability of TAM relationships across contexts. The findings show that core TAM relationships are consistently positive and stable at the aggregate level yet display substantial variation across empirical settings. While some relationships remain robust across contexts, others exhibit prediction intervals that include zero, indicating that their strength and even direction may depend on contextual conditions. As prior TAM meta-analyses have not systematically incorporated prediction intervals, this study provides new evidence to the extent to which TAM relationships generalize beyond average effects. The results further show that although TAM offers a reliable structural framework, interventions and policies based on its core relationships must be context-sensitive, because relying on average effects alone may lead to ineffective or inconsistent adoption outcomes. Full article

Milk is a complex biochemical mixture in which proteins significantly influence the behaviour of xenobiotics and bioactive compounds. Interactions between milk proteins and substances such as veterinary drugs or natural bioactives can modify molecular stability, binding dynamics, and exposure pathways, affecting food safety and the One Health concept. This study presents a comparative, matrix-focused investigation on how three chemically distinct ligand classes, sulfanilamide antibiotics, naturally occurring phenolic compounds and zinc–polyphenol complexes, interact with major milk proteins, β-lactoglobulin and casein. Protein–ligand interactions were examined using steady-state fluorescence spectroscopy to assess quenching behaviour and comparative interaction trends. Molecular docking was employed as a qualitative tool to provide structural context. Distinct interaction patterns were observed across ligand classes, reflecting differences in molecular structure, hydrophobicity, and coordination chemistry. Importantly, zinc coordination modified interaction profiles relative to the corresponding free ligands, indicating that metal coordination can affect ligand–protein interactions within the milk matrix. These findings support the concept that milk proteins may function as matrix-dependent modulators of ligand behaviour. The study is positioned as a hypothesis-generating framework highlighting the importance of food matrices as active biochemical environments. Herein, we provide a foundation for hypothesising how the milk matrix affects residue behaviour and bioactive interactions, with relevance to veterinary pharmacology and food safety risk assessment. Full article

To support the safe operation and technological promotion of existing line speed-up projects, this paper presents an assessment method for pantograph–catenary contact performance under the 200 km/h speed conditions, using the Guangzhou–Shenzhen Lines I and II speed-up projects as representative case studies. Based on the ANCF method, a refined pantograph–catenary coupling dynamic model is established to accurately characterize the large deformation and geometric nonlinear behavior of the catenary system. Model validation is achieved using actual measurement data from the CR400AF train. Based on this model, systematic simulation analyses were conducted to evaluate the current collection performance of four mainstream train models—CR300AF, CR400BF, CRH380A, and CRH380B—under both single-unit and double-unit operation conditions. Results indicate that dynamic contact force metrics for pantograph–catenary interactions meet all limit requirements specified in the Technical Specifications for Dynamic Acceptance of High-Speed Railway Projects under all operating conditions. This demonstrates that the pantograph–catenary system on the analyzed Guangzhou–Shenzhen Line exhibits excellent dynamic stability and safety under the targeted speed-up scheme, providing simulation-based justification for implementing the speed enhancement project. Full article

Background/Objectives: Macular pigment optical density (MPOD) represents a biomarker of retinal antioxidant status in intermediate age-related macular degeneration (iAMD). Strategies aimed at increasing macular carotenoid availability may contribute to disease stabilization. This study evaluated the effects of oral supplementation and transscleral iontophoresis on MPOD and retinal parameters in iAMD. Methods: This prospective, non-randomized pilot study included 60 eyes of 60 patients with intermediate AMD enrolled at the Eye Clinic of the University of Naples Federico II between July 2024 and May 2025 (ClinicalTrials.gov NCT06465342). Patients received either oral carotenoid supplementation (n = 30) or transscleral iontophoresis (n = 30). Best-corrected visual acuity (BCVA), central macular thickness (CMT), and MPOD measured by one-wavelength reflectometry ( Visucam 200; Carl Zeiss Meditec, Jena, Germany) were assessed at baseline and 6 months. Results: BCVA remained stable in both groups without significant changes (p > 0.05). MPOD significantly increased in the iontophoresis group (0.40 ± 0.11 to 0.49 ± 0.12, p < 0.001) with no statistically significant difference between them (p = 0.09). CMT showed a mild, non-significant increase in both groups (p > 0.05). No adverse events were observed. Conclusions: Both oral supplementation and transscleral iontophoresis were associated with a significant increase in MPOD while preserving visual acuity in intermediate AMD. Within the limitations of this non-randomized pilot study, transscleral iontophoresis produced MPOD changes comparable to those observed with oral supplementation. These findings are exploratory and support further investigation of localized delivery strategies in larger, randomized trials. Full article

Apical prolapse, a common form of Pelvic Organ Prolapse (POP), is often linked to weakened support structures such as the uterosacral (USL) and cardinal ligaments (CL), influenced by factors like vaginal childbirth, aging, and obesity. Although surgical mesh use is expected to increase, the Food and Drug Administration (FDA) banned polypropylene mesh for transvaginal anterior compartment prolapse in 2019 due to safety concerns, highlighting the need for alternatives such as biodegradable implants. This study developed four biodegradable mesh implants (square and sinusoidal geometries) mimicking the USL and CL. These were applied within a computational pelvic model to assess biomechanical behavior during the Valsalva maneuver and to explore different fixation methods (continuous, interrupted and simple stitch sutures). Baseline analysis of the healthy model established vaginal displacement under normal conditions. Without implant support, complete CL rupture increased displacement by 34%, and complete USL rupture raised displacement by 69%. Polycaprolactone implants consistently reduced anterior vaginal wall displacement in all impairment scenarios. Square implants mimicking the USL reduced displacement by up to 10% in cases of complete USL rupture with intact CL. Similarly, square implants mimicking the CL reduced displacement by up to 15% with complete CL rupture and healthy USL. Simulations with both ligaments impaired showed that USL contribute to support, while CL play a key role in stabilization. These findings demonstrate the potential of biodegradable implants to enhance POP repair. However, further studies are needed to evaluate long-term degradation and clinical applicability. Full article

Microalgae and cyanobacteria are photosynthetic microorganisms capable of removing nutrients from eutrophic waters and producing biomass. Therefore, the aim of this study was to evaluate the bioremediation performance of three microalgae and one cyanobacterium native to Lake Xochimilco and to assess their potential for biofuel production (biodiesel and biogas) from biomass generated. In photobioreactors, ammonium (96.61–97.06%), nitrate (82.4–100%), and phosphate (83.95–89.71%) were effectively removed from the lake water. The specific growth rates ranged from 0.041 to 0.144 d⁻¹ and biomass productivities from 0.016 to 0.049 g L⁻¹ d⁻¹, with high biomass yield on the substrate. The estimated CO₂ fixation rates ranged from 0.024 to 0.092 g L⁻¹ d⁻¹. Chlorella sp. achieved the highest yield of fatty acid methyl esters (FAMEs) with 91.24% of the extracted lipids. Overall, saturated FAMEs were predominant in the biodiesel; however, the presence of monounsaturated FAMEs such as methyl palmitoleate and methyl oleate enhances their fluidity and oxidative stability. Synechocystis sp. and Chlorella sp. produced the most biogas using biomass after lipid extraction, at 429.5 L kg⁻¹ VS and 404.9 L kg⁻¹ VS, respectively, with over 60% biomethane. These strains represent a sustainable and promising possibility for water bioremediation and generating biofuels. Full article

Unmanned Aerial Vehicles (UAVs) are complex nonlinear systems characterized by high dimensionality. They are prone to aerodynamic effects, structural dynamics, actuation constraints, and networked interactions, requiring advanced mathematical models and precise control. Their governing equations involve nonlinear rigid-body dynamics coupled with fluid and elasticity models, while modern architectures introduce redundancy that creates constrained mappings between generalized forces and actuator inputs. Coordinated UAV teams add another layer of mathematical structure through graph-based interaction models that determine consensus, formation keeping, and distributed stability. These characteristics give rise to several interconnected challenges. High-fidelity aerodynamic and aeroelastic solvers provide accurate results; however, these are computationally intensive, motivating the development of reduced-order models and data-driven approximations that preserve dominant physical behavior. Methods for quantifying uncertainty support robustness assessments by characterizing the effects of parametric variation and model form error. At the actuation level, control allocation problems rely on constrained linear algebra, convex optimization, and dynamic formulations to ensure feasible and stable realization of command forces and moments. In multi-agent systems, the spectral properties of adjacency and Laplacian matrices govern convergence and cooperative behavior. This article reviews the state of the art in these areas, highlights the mathematical foundations that relate them, and provides a coherent perspective on the methods that enable reliable modeling and control of modern UAV systems. Full article